Overview

Dataset statistics

Number of variables34
Number of observations9694
Missing cells1537
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory258.0 B

Variable types

Numeric14
Categorical14
Boolean6

Alerts

diag_1 has a high cardinality: 447 distinct values High cardinality
diag_2 has a high cardinality: 441 distinct values High cardinality
diag_3 has a high cardinality: 468 distinct values High cardinality
change is highly correlated with diabetesMedHigh correlation
insulin is highly correlated with diabetesMedHigh correlation
diabetesMed is highly correlated with change and 1 other fieldsHigh correlation
gender is highly correlated with hemoglobin_levelHigh correlation
age is highly correlated with medical_specialtyHigh correlation
admission_type_code is highly correlated with admission_source_code and 1 other fieldsHigh correlation
admission_source_code is highly correlated with admission_type_code and 1 other fieldsHigh correlation
medical_specialty is highly correlated with age and 2 other fieldsHigh correlation
hemoglobin_level is highly correlated with genderHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
age has 293 (3.0%) missing values Missing
weight has 166 (1.7%) missing values Missing
admission_type_code has 146 (1.5%) missing values Missing
num_lab_procedures has 184 (1.9%) missing values Missing
num_medications has 308 (3.2%) missing values Missing
diag_2 has 169 (1.7%) missing values Missing
readmitted has 200 (2.1%) missing values Missing
number_emergency is highly skewed (γ1 = 32.69060597) Skewed
admission_id has unique values Unique
num_procedures has 4385 (45.2%) zeros Zeros
number_outpatient has 8093 (83.5%) zeros Zeros
number_emergency has 8613 (88.8%) zeros Zeros
number_inpatient has 6490 (66.9%) zeros Zeros

Reproduction

Analysis started2022-03-04 15:31:59.845751
Analysis finished2022-03-04 15:32:37.344006
Duration37.5 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

admission_id
Real number (ℝ≥0)

UNIQUE

Distinct9694
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91396.53858
Minimum81412
Maximum101440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:37.491717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum81412
5-th percentile82456.95
Q186442.25
median91364
Q396385.25
95-th percentile100380.35
Maximum101440
Range20028
Interquartile range (IQR)9943

Descriptive statistics

Standard deviation5745.128963
Coefficient of variation (CV)0.0628593714
Kurtosis-1.193941082
Mean91396.53858
Median Absolute Deviation (MAD)4974
Skewness0.003909738519
Sum885998045
Variance33006506.8
MonotonicityNot monotonic
2022-03-04T15:32:37.646879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
927711
 
< 0.1%
1014351
 
< 0.1%
890331
 
< 0.1%
868851
 
< 0.1%
980931
 
< 0.1%
891831
 
< 0.1%
816671
 
< 0.1%
892821
 
< 0.1%
880761
 
< 0.1%
838241
 
< 0.1%
Other values (9684)9684
99.9%
ValueCountFrequency (%)
814121
< 0.1%
814151
< 0.1%
814171
< 0.1%
814181
< 0.1%
814191
< 0.1%
814201
< 0.1%
814261
< 0.1%
814271
< 0.1%
814291
< 0.1%
814311
< 0.1%
ValueCountFrequency (%)
1014401
< 0.1%
1014381
< 0.1%
1014371
< 0.1%
1014351
< 0.1%
1014321
< 0.1%
1014301
< 0.1%
1014291
< 0.1%
1014221
< 0.1%
1014161
< 0.1%
1014151
< 0.1%

patient_id
Real number (ℝ≥0)

Distinct9219
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108440221.2
Minimum10368
Maximum378731656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:37.801802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10368
5-th percentile2786253.3
Q146740537
median91137042
Q3175249750.5
95-th percentile222658891.2
Maximum378731656
Range378721288
Interquartile range (IQR)128509213.5

Descriptive statistics

Standard deviation77557704.94
Coefficient of variation (CV)0.7152116076
Kurtosis-0.3056385962
Mean108440221.2
Median Absolute Deviation (MAD)66517875
Skewness0.4817043225
Sum1.051219505 × 1012
Variance6.015197596 × 1015
MonotonicityNot monotonic
2022-03-04T15:32:38.039029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
862817406
 
0.1%
30613685
 
0.1%
7698065
 
0.1%
850268524
 
< 0.1%
741936604
 
< 0.1%
467969044
 
< 0.1%
1775793424
 
< 0.1%
1864113123
 
< 0.1%
1845547563
 
< 0.1%
994402443
 
< 0.1%
Other values (9209)9653
99.6%
ValueCountFrequency (%)
103681
< 0.1%
133201
< 0.1%
133741
< 0.1%
163441
< 0.1%
229501
< 0.1%
248221
< 0.1%
260101
< 0.1%
295201
< 0.1%
374581
< 0.1%
442441
< 0.1%
ValueCountFrequency (%)
3787316561
< 0.1%
3785156201
< 0.1%
3783907721
< 0.1%
3783389501
< 0.1%
3774073241
< 0.1%
3765696941
< 0.1%
3762382781
< 0.1%
3758470481
< 0.1%
3735359381
< 0.1%
3735146081
< 0.1%

race
Categorical

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
Caucasian
5840 
AfricanAmerican
1096 
White
701 
WHITE
 
367
African American
 
352
Other values (10)
1338 

Length

Max length16
Median length9
Mean length9.136476171
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowAfrican American
3rd rowAfricanAmerican
4th rowCaucasian
5th rowHispanic

Common Values

ValueCountFrequency (%)
Caucasian5840
60.2%
AfricanAmerican1096
 
11.3%
White701
 
7.2%
WHITE367
 
3.8%
African American352
 
3.6%
European264
 
2.7%
?227
 
2.3%
Afro American174
 
1.8%
Other168
 
1.7%
Hispanic158
 
1.6%
Other values (5)347
 
3.6%

Length

2022-03-04T15:32:38.284621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caucasian5840
57.1%
africanamerican1115
 
10.9%
white1068
 
10.5%
american526
 
5.1%
african352
 
3.4%
european264
 
2.6%
227
 
2.2%
afro174
 
1.7%
other168
 
1.6%
hispanic158
 
1.5%
Other values (4)328
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
Female
5239 
Male
4454 
Unknown/Invalid
 
1

Length

Max length15
Median length6
Mean length5.08200949
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female5239
54.0%
Male4454
45.9%
Unknown/Invalid1
 
< 0.1%

Length

2022-03-04T15:32:38.422802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:38.495714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
female5239
54.0%
male4454
45.9%
unknown/invalid1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.1%
Missing293
Missing (%)3.0%
Memory size75.9 KiB
[70-80)
2471 
[60-70)
2113 
[50-60)
1606 
[80-90)
1498 
[40-50)
914 
Other values (5)
799 

Length

Max length8
Median length7
Mean length7.024997341
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[40-50)
2nd row[60-70)
3rd row[60-70)
4th row[50-60)
5th row[30-40)

Common Values

ValueCountFrequency (%)
[70-80)2471
25.5%
[60-70)2113
21.8%
[50-60)1606
16.6%
[80-90)1498
15.5%
[40-50)914
 
9.4%
[30-40)336
 
3.5%
[90-100)245
 
2.5%
[20-30)150
 
1.5%
[10-20)58
 
0.6%
[0-10)10
 
0.1%
(Missing)293
 
3.0%

Length

2022-03-04T15:32:38.599349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:38.695875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
70-802471
26.3%
60-702113
22.5%
50-601606
17.1%
80-901498
15.9%
40-50914
 
9.7%
30-40336
 
3.6%
90-100245
 
2.6%
20-30150
 
1.6%
10-2058
 
0.6%
0-1010
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

weight
Categorical

MISSING

Distinct9
Distinct (%)0.1%
Missing166
Missing (%)1.7%
Memory size75.9 KiB
?
9223 
[75-100)
 
130
[50-75)
 
75
[100-125)
 
68
[125-150)
 
15
Other values (4)
 
17

Length

Max length9
Median length1
Mean length1.223971453
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?9223
95.1%
[75-100)130
 
1.3%
[50-75)75
 
0.8%
[100-125)68
 
0.7%
[125-150)15
 
0.2%
[25-50)10
 
0.1%
[150-175)4
 
< 0.1%
[0-25)2
 
< 0.1%
[175-200)1
 
< 0.1%
(Missing)166
 
1.7%

Length

2022-03-04T15:32:38.875399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:38.969751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
9223
96.8%
75-100130
 
1.4%
50-7575
 
0.8%
100-12568
 
0.7%
125-15015
 
0.2%
25-5010
 
0.1%
150-1754
 
< 0.1%
0-252
 
< 0.1%
175-2001
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

admission_type_code
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing146
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.034352744
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:39.375782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.445375352
Coefficient of variation (CV)0.7104841363
Kurtosis1.961745858
Mean2.034352744
Median Absolute Deviation (MAD)0
Skewness1.586107859
Sum19424
Variance2.089109909
MonotonicityNot monotonic
2022-03-04T15:32:39.474831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15003
51.6%
31811
 
18.7%
21759
 
18.1%
6492
 
5.1%
5448
 
4.6%
833
 
0.3%
72
 
< 0.1%
(Missing)146
 
1.5%
ValueCountFrequency (%)
15003
51.6%
21759
 
18.1%
31811
 
18.7%
5448
 
4.6%
6492
 
5.1%
72
 
< 0.1%
833
 
0.3%
ValueCountFrequency (%)
833
 
0.3%
72
 
< 0.1%
6492
 
5.1%
5448
 
4.6%
31811
 
18.7%
21759
 
18.1%
15003
51.6%

discharge_disposition_code
Real number (ℝ≥0)

Distinct21
Distinct (%)0.2%
Missing71
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.612075236
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:39.586623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.257423183
Coefficient of variation (CV)1.455513199
Kurtosis6.464387079
Mean3.612075236
Median Absolute Deviation (MAD)0
Skewness2.667215004
Sum34759
Variance27.64049853
MonotonicityNot monotonic
2022-03-04T15:32:39.706654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15786
59.7%
31342
 
13.8%
61244
 
12.8%
18379
 
3.9%
2202
 
2.1%
22181
 
1.9%
5118
 
1.2%
2595
 
1.0%
470
 
0.7%
755
 
0.6%
Other values (11)151
 
1.6%
(Missing)71
 
0.7%
ValueCountFrequency (%)
15786
59.7%
2202
 
2.1%
31342
 
13.8%
470
 
0.7%
5118
 
1.2%
61244
 
12.8%
755
 
0.6%
811
 
0.1%
91
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
2812
 
0.1%
271
 
< 0.1%
2595
 
1.0%
2350
 
0.5%
22181
1.9%
18379
3.9%
171
 
< 0.1%
161
 
< 0.1%
154
 
< 0.1%
1433
 
0.3%

admission_source_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.739942232
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:39.811224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum20
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.072911705
Coefficient of variation (CV)0.7095736403
Kurtosis1.695898731
Mean5.739942232
Median Absolute Deviation (MAD)0
Skewness1.024906791
Sum55643
Variance16.58860976
MonotonicityNot monotonic
2022-03-04T15:32:39.971991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
75468
56.4%
12841
29.3%
17651
 
6.7%
4302
 
3.1%
6192
 
2.0%
2114
 
1.2%
576
 
0.8%
319
 
0.2%
914
 
0.1%
2014
 
0.1%
Other values (2)3
 
< 0.1%
ValueCountFrequency (%)
12841
29.3%
2114
 
1.2%
319
 
0.2%
4302
 
3.1%
576
 
0.8%
6192
 
2.0%
75468
56.4%
81
 
< 0.1%
914
 
0.1%
102
 
< 0.1%
ValueCountFrequency (%)
2014
 
0.1%
17651
 
6.7%
102
 
< 0.1%
914
 
0.1%
81
 
< 0.1%
75468
56.4%
6192
 
2.0%
576
 
0.8%
4302
 
3.1%
319
 
0.2%

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.389725603
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:40.134948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.004512179
Coefficient of variation (CV)0.6844419107
Kurtosis0.8095413805
Mean4.389725603
Median Absolute Deviation (MAD)2
Skewness1.130620534
Sum42554
Variance9.027093435
MonotonicityNot monotonic
2022-03-04T15:32:40.259136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
31692
17.5%
21656
17.1%
11388
14.3%
41275
13.2%
5945
9.7%
6720
7.4%
7544
 
5.6%
8421
 
4.3%
9293
 
3.0%
10214
 
2.2%
Other values (4)546
 
5.6%
ValueCountFrequency (%)
11388
14.3%
21656
17.1%
31692
17.5%
41275
13.2%
5945
9.7%
6720
7.4%
7544
 
5.6%
8421
 
4.3%
9293
 
3.0%
10214
 
2.2%
ValueCountFrequency (%)
1493
 
1.0%
13128
 
1.3%
12139
 
1.4%
11186
 
1.9%
10214
 
2.2%
9293
 
3.0%
8421
4.3%
7544
5.6%
6720
7.4%
5945
9.7%

payer_code
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
?
3843 
MC
3038 
HM
617 
SP
491 
BC
453 
Other values (12)
1252 

Length

Max length2
Median length2
Mean length1.603569218
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBC
2nd row?
3rd rowBC
4th rowMC
5th row?

Common Values

ValueCountFrequency (%)
?3843
39.6%
MC3038
31.3%
HM617
 
6.4%
SP491
 
5.1%
BC453
 
4.7%
MD332
 
3.4%
UN244
 
2.5%
CP213
 
2.2%
CM195
 
2.0%
OG120
 
1.2%
Other values (7)148
 
1.5%

Length

2022-03-04T15:32:40.386803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3843
39.6%
mc3038
31.3%
hm617
 
6.4%
sp491
 
5.1%
bc453
 
4.7%
md332
 
3.4%
un244
 
2.5%
cp213
 
2.2%
cm195
 
2.0%
og120
 
1.2%
Other values (7)148
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

medical_specialty
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
?
4707 
InternalMedicine
1355 
Emergency/Trauma
759 
Family/GeneralPractice
738 
Cardiology
507 
Other values (45)
1628 

Length

Max length36
Median length9
Mean length8.737466474
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowEndocrinology
2nd row?
3rd rowFamily/GeneralPractice
4th rowRadiologist
5th row?

Common Values

ValueCountFrequency (%)
?4707
48.6%
InternalMedicine1355
 
14.0%
Emergency/Trauma759
 
7.8%
Family/GeneralPractice738
 
7.6%
Cardiology507
 
5.2%
Surgery-General299
 
3.1%
Orthopedics139
 
1.4%
Orthopedics-Reconstructive132
 
1.4%
Radiologist125
 
1.3%
Nephrology122
 
1.3%
Other values (40)811
 
8.4%

Length

2022-03-04T15:32:40.545397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4707
48.6%
internalmedicine1355
 
14.0%
emergency/trauma759
 
7.8%
family/generalpractice738
 
7.6%
cardiology507
 
5.2%
surgery-general299
 
3.1%
orthopedics139
 
1.4%
orthopedics-reconstructive132
 
1.4%
radiologist125
 
1.3%
nephrology122
 
1.3%
Other values (40)811
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
9599 
True
 
95
ValueCountFrequency (%)
False9599
99.0%
True95
 
1.0%
2022-03-04T15:32:40.645123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
Complete
8106 
Incomplete
1550 
None
 
38

Length

Max length10
Median length8
Mean length8.304105632
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowComplete
3rd rowComplete
4th rowComplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete8106
83.6%
Incomplete1550
 
16.0%
None38
 
0.4%

Length

2022-03-04T15:32:40.770953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:40.857940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
complete8106
83.6%
incomplete1550
 
16.0%
none38
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_lab_procedures
Real number (ℝ≥0)

MISSING

Distinct105
Distinct (%)1.1%
Missing184
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean42.94090431
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:40.965450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q131
median44
Q357
95-th percentile73
Maximum111
Range110
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.86203542
Coefficient of variation (CV)0.4625434824
Kurtosis-0.2834919112
Mean42.94090431
Median Absolute Deviation (MAD)13
Skewness-0.2405578687
Sum408368
Variance394.500451
MonotonicityNot monotonic
2022-03-04T15:32:41.156104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320
 
3.3%
43255
 
2.6%
42219
 
2.3%
49218
 
2.2%
45217
 
2.2%
44216
 
2.2%
39206
 
2.1%
40206
 
2.1%
46203
 
2.1%
37198
 
2.0%
Other values (95)7252
74.8%
ValueCountFrequency (%)
1320
3.3%
2109
 
1.1%
356
 
0.6%
437
 
0.4%
531
 
0.3%
634
 
0.4%
724
 
0.2%
843
 
0.4%
985
 
0.9%
1092
 
0.9%
ValueCountFrequency (%)
1111
 
< 0.1%
1091
 
< 0.1%
1071
 
< 0.1%
1031
 
< 0.1%
1021
 
< 0.1%
1012
< 0.1%
1003
< 0.1%
981
 
< 0.1%
974
< 0.1%
961
 
< 0.1%

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.349391376
Minimum0
Maximum6
Zeros4385
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:41.279784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.703723337
Coefficient of variation (CV)1.262586502
Kurtosis0.836890554
Mean1.349391376
Median Absolute Deviation (MAD)1
Skewness1.307333976
Sum13081
Variance2.902673208
MonotonicityNot monotonic
2022-03-04T15:32:41.380007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04385
45.2%
12007
20.7%
21234
 
12.7%
3914
 
9.4%
6461
 
4.8%
4367
 
3.8%
5326
 
3.4%
ValueCountFrequency (%)
04385
45.2%
12007
20.7%
21234
 
12.7%
3914
 
9.4%
4367
 
3.8%
5326
 
3.4%
6461
 
4.8%
ValueCountFrequency (%)
6461
 
4.8%
5326
 
3.4%
4367
 
3.8%
3914
 
9.4%
21234
 
12.7%
12007
20.7%
04385
45.2%

num_medications
Real number (ℝ≥0)

MISSING

Distinct64
Distinct (%)0.7%
Missing308
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean16.0644577
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:41.620877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum79
Range78
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.277705312
Coefficient of variation (CV)0.5152807188
Kurtosis3.558316821
Mean16.0644577
Median Absolute Deviation (MAD)5
Skewness1.382377006
Sum150781
Variance68.52040523
MonotonicityNot monotonic
2022-03-04T15:32:41.916868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15568
 
5.9%
13566
 
5.8%
12557
 
5.7%
11551
 
5.7%
10510
 
5.3%
14502
 
5.2%
16482
 
5.0%
9445
 
4.6%
17445
 
4.6%
18406
 
4.2%
Other values (54)4354
44.9%
ValueCountFrequency (%)
126
 
0.3%
251
 
0.5%
383
 
0.9%
4109
 
1.1%
5174
 
1.8%
6242
2.5%
7355
3.7%
8406
4.2%
9445
4.6%
10510
5.3%
ValueCountFrequency (%)
791
 
< 0.1%
661
 
< 0.1%
651
 
< 0.1%
614
< 0.1%
603
< 0.1%
593
< 0.1%
586
0.1%
573
< 0.1%
564
< 0.1%
553
< 0.1%

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3634206726
Minimum0
Maximum21
Zeros8093
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:42.159327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.187414181
Coefficient of variation (CV)3.267327014
Kurtosis70.18884936
Mean0.3634206726
Median Absolute Deviation (MAD)0
Skewness6.716536954
Sum3523
Variance1.409952437
MonotonicityNot monotonic
2022-03-04T15:32:42.312100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
08093
83.5%
1824
 
8.5%
2330
 
3.4%
3212
 
2.2%
4106
 
1.1%
549
 
0.5%
630
 
0.3%
811
 
0.1%
78
 
0.1%
115
 
0.1%
Other values (9)26
 
0.3%
ValueCountFrequency (%)
08093
83.5%
1824
 
8.5%
2330
 
3.4%
3212
 
2.2%
4106
 
1.1%
549
 
0.5%
630
 
0.3%
78
 
0.1%
811
 
0.1%
95
 
0.1%
ValueCountFrequency (%)
212
 
< 0.1%
191
 
< 0.1%
173
< 0.1%
161
 
< 0.1%
154
< 0.1%
144
< 0.1%
132
 
< 0.1%
115
0.1%
104
< 0.1%
95
0.1%

number_emergency
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1893955024
Minimum0
Maximum63
Zeros8613
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:42.427294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.952079387
Coefficient of variation (CV)5.026937678
Kurtosis1978.568005
Mean0.1893955024
Median Absolute Deviation (MAD)0
Skewness32.69060597
Sum1836
Variance0.9064551592
MonotonicityNot monotonic
2022-03-04T15:32:42.706957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
08613
88.8%
1747
 
7.7%
2195
 
2.0%
357
 
0.6%
442
 
0.4%
58
 
0.1%
78
 
0.1%
86
 
0.1%
66
 
0.1%
104
 
< 0.1%
Other values (5)8
 
0.1%
ValueCountFrequency (%)
08613
88.8%
1747
 
7.7%
2195
 
2.0%
357
 
0.6%
442
 
0.4%
58
 
0.1%
66
 
0.1%
78
 
0.1%
86
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
631
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
112
 
< 0.1%
104
< 0.1%
92
 
< 0.1%
86
0.1%
78
0.1%
66
0.1%
58
0.1%

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6262636682
Minimum0
Maximum16
Zeros6490
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:42.864282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.254516375
Coefficient of variation (CV)2.00317604
Kurtosis21.20301943
Mean0.6262636682
Median Absolute Deviation (MAD)0
Skewness3.647348129
Sum6071
Variance1.573811335
MonotonicityNot monotonic
2022-03-04T15:32:43.032299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
06490
66.9%
11837
 
18.9%
2694
 
7.2%
3326
 
3.4%
4156
 
1.6%
585
 
0.9%
647
 
0.5%
817
 
0.2%
716
 
0.2%
108
 
0.1%
Other values (7)18
 
0.2%
ValueCountFrequency (%)
06490
66.9%
11837
 
18.9%
2694
 
7.2%
3326
 
3.4%
4156
 
1.6%
585
 
0.9%
647
 
0.5%
716
 
0.2%
817
 
0.2%
96
 
0.1%
ValueCountFrequency (%)
161
 
< 0.1%
152
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
114
 
< 0.1%
108
0.1%
96
 
0.1%
817
0.2%
716
0.2%

diag_1
Categorical

HIGH CARDINALITY

Distinct447
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
428
 
685
414
 
669
786
 
372
410
 
321
486
 
318
Other values (442)
7329 

Length

Max length6
Median length3
Mean length3.166082113
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)1.1%

Sample

1st row577
2nd row428
3rd row562
4th row414
5th row808

Common Values

ValueCountFrequency (%)
428685
 
7.1%
414669
 
6.9%
786372
 
3.8%
410321
 
3.3%
486318
 
3.3%
427272
 
2.8%
491231
 
2.4%
715229
 
2.4%
682212
 
2.2%
434203
 
2.1%
Other values (437)6182
63.8%

Length

2022-03-04T15:32:43.295885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428685
 
7.1%
414669
 
6.9%
786372
 
3.8%
410321
 
3.3%
486318
 
3.3%
427272
 
2.8%
491231
 
2.4%
715229
 
2.4%
682212
 
2.2%
434203
 
2.1%
Other values (437)6182
63.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_2
Categorical

HIGH CARDINALITY
MISSING

Distinct441
Distinct (%)4.6%
Missing169
Missing (%)1.7%
Memory size75.9 KiB
428
 
654
276
 
615
250
 
598
427
 
462
401
 
368
Other values (436)
6828 

Length

Max length6
Median length3
Mean length3.165984252
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)1.4%

Sample

1st row250.02
2nd row585
3rd row596
4th row411
5th row861

Common Values

ValueCountFrequency (%)
428654
 
6.7%
276615
 
6.3%
250598
 
6.2%
427462
 
4.8%
401368
 
3.8%
599300
 
3.1%
496292
 
3.0%
414283
 
2.9%
411258
 
2.7%
403242
 
2.5%
Other values (431)5453
56.3%

Length

2022-03-04T15:32:43.518676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428654
 
6.9%
276615
 
6.5%
250598
 
6.3%
427462
 
4.9%
401368
 
3.9%
599300
 
3.1%
496292
 
3.1%
414283
 
3.0%
411258
 
2.7%
403242
 
2.5%
Other values (431)5453
57.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_3
Categorical

HIGH CARDINALITY

Distinct468
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
250
1169 
401
839 
276
 
511
428
 
396
427
 
384
Other values (463)
6395 

Length

Max length6
Median length3
Mean length3.105426037
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)1.4%

Sample

1st row401
2nd row496
3rd row585
4th row295
5th row865

Common Values

ValueCountFrequency (%)
2501169
 
12.1%
401839
 
8.7%
276511
 
5.3%
428396
 
4.1%
427384
 
4.0%
414376
 
3.9%
496232
 
2.4%
403198
 
2.0%
272191
 
2.0%
599188
 
1.9%
Other values (458)5210
53.7%

Length

2022-03-04T15:32:43.820256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2501169
 
12.1%
401839
 
8.7%
276511
 
5.3%
428396
 
4.1%
427384
 
4.0%
414376
 
3.9%
496232
 
2.4%
403198
 
2.0%
272191
 
2.0%
599188
 
1.9%
Other values (458)5210
53.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_diagnoses
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.41252321
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:43.946741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.954272453
Coefficient of variation (CV)0.2636446993
Kurtosis-0.1242502461
Mean7.41252321
Median Absolute Deviation (MAD)1
Skewness-0.9111943497
Sum71857
Variance3.819180819
MonotonicityNot monotonic
2022-03-04T15:32:44.067780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
94746
49.0%
51038
 
10.7%
81013
 
10.4%
6984
 
10.2%
7935
 
9.6%
4542
 
5.6%
3298
 
3.1%
2109
 
1.1%
122
 
0.2%
162
 
< 0.1%
Other values (4)5
 
0.1%
ValueCountFrequency (%)
122
 
0.2%
2109
 
1.1%
3298
 
3.1%
4542
 
5.6%
51038
 
10.7%
6984
 
10.2%
7935
 
9.6%
81013
 
10.4%
94746
49.0%
101
 
< 0.1%
ValueCountFrequency (%)
162
 
< 0.1%
152
 
< 0.1%
141
 
< 0.1%
121
 
< 0.1%
101
 
< 0.1%
94746
49.0%
81013
 
10.4%
7935
 
9.6%
6984
 
10.2%
51038
 
10.7%

blood_type
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
O+
3799 
A+
2968 
B+
1074 
O-
702 
A-
578 
Other values (3)
573 

Length

Max length3
Median length2
Mean length2.042603672
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowO-
3rd rowA+
4th rowO-
5th rowO+

Common Values

ValueCountFrequency (%)
O+3799
39.2%
A+2968
30.6%
B+1074
 
11.1%
O-702
 
7.2%
A-578
 
6.0%
AB+321
 
3.3%
B-160
 
1.7%
AB-92
 
0.9%

Length

2022-03-04T15:32:44.184965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:44.266331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
o4501
46.4%
a3546
36.6%
b1234
 
12.7%
ab413
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hemoglobin_level
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.18680627
Minimum10.9
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.9 KiB
2022-03-04T15:32:44.401173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10.9
5-th percentile12.6
Q113.4
median14.1
Q314.9
95-th percentile16
Maximum18
Range7.1
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.050907971
Coefficient of variation (CV)0.07407643069
Kurtosis-0.4462990845
Mean14.18680627
Median Absolute Deviation (MAD)0.8
Skewness0.189357108
Sum137526.9
Variance1.104407564
MonotonicityNot monotonic
2022-03-04T15:32:44.568805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.6354
 
3.7%
13.8348
 
3.6%
13.5337
 
3.5%
14.5326
 
3.4%
13.7323
 
3.3%
13.4318
 
3.3%
13.9316
 
3.3%
14315
 
3.2%
14.4313
 
3.2%
14.3313
 
3.2%
Other values (60)6431
66.3%
ValueCountFrequency (%)
10.91
 
< 0.1%
11.11
 
< 0.1%
11.22
 
< 0.1%
11.33
 
< 0.1%
11.42
 
< 0.1%
11.52
 
< 0.1%
11.67
 
0.1%
11.711
0.1%
11.813
0.1%
11.922
0.2%
ValueCountFrequency (%)
181
 
< 0.1%
17.81
 
< 0.1%
17.72
 
< 0.1%
17.61
 
< 0.1%
17.52
 
< 0.1%
17.43
 
< 0.1%
17.31
 
< 0.1%
17.21
 
< 0.1%
17.18
0.1%
178
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
8522 
True
1172 
ValueCountFrequency (%)
False8522
87.9%
True1172
 
12.1%
2022-03-04T15:32:44.669120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

max_glu_serum
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
None
6442 
NONE
2744 
Norm
 
166
>200
 
147
>300
 
101

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNONE
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None6442
66.5%
NONE2744
28.3%
Norm166
 
1.7%
>200147
 
1.5%
>300101
 
1.0%
NORM94
 
1.0%

Length

2022-03-04T15:32:44.741719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:44.811894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none9186
94.8%
norm260
 
2.7%
200147
 
1.5%
300101
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
None
8070 
>8
 
798
Norm
 
478
>7
 
348

Length

Max length4
Median length4
Mean length3.763565092
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>8
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None8070
83.2%
>8798
 
8.2%
Norm478
 
4.9%
>7348
 
3.6%

Length

2022-03-04T15:32:44.929883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:45.018120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none8070
83.2%
8798
 
8.2%
norm478
 
4.9%
7348
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diuretics
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
9515 
True
 
179
ValueCountFrequency (%)
False9515
98.2%
True179
 
1.8%
2022-03-04T15:32:45.078175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

insulin
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
5257 
False
4437 
ValueCountFrequency (%)
True5257
54.2%
False4437
45.8%
2022-03-04T15:32:45.124311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

change
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
No
5165 
Ch
4529 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowCh
5th rowCh

Common Values

ValueCountFrequency (%)
No5165
53.3%
Ch4529
46.7%

Length

2022-03-04T15:32:45.210112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T15:32:45.279827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no5165
53.3%
ch4529
46.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetesMed
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
7493 
False
2201 
ValueCountFrequency (%)
True7493
77.3%
False2201
 
22.7%
2022-03-04T15:32:45.321088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

readmitted
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing200
Missing (%)2.1%
Memory size75.9 KiB
False
8420 
True
1074 
(Missing)
 
200
ValueCountFrequency (%)
False8420
86.9%
True1074
 
11.1%
(Missing)200
 
2.1%
2022-03-04T15:32:45.363628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2022-03-04T15:32:33.099233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:06.835847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:08.964979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.976278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.058519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.991687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.022553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.075114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.006776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.090301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.122194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.990435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.927229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.112618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:33.243197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:06.996649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.108081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.120469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.204014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:15.137328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.290625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.212645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.152506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.238115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.257572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.137609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:29.072919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.258197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:33.389066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.140811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.250655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.261018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.344715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:15.287874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.431460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.350155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.291370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.384977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.397149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.280843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:29.217218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.404220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:33.528612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.291469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.396360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.402958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.486400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:15.436818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.572258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.487959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.427745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.528979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.531549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.418798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:29.360234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.548109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:33.665718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.428029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.536521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.544960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.618991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:15.581111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.701422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.621729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.558704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.670005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.665333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.552453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:29.496723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.686488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:33.811739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.575014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.679301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.701546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.761711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:15.731123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.843232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.761948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.706997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.820641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.804643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.695296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:29.642731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.842087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:33.951572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.716317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.819842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.837639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:13.900134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:15.879922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:17.985269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:19.899470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.848443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:23.963120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:25.939377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.833061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:29.781443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:31.983167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.087982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.852894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:09.960938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:11.973864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.031842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.017887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.116512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.032805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:21.979908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.100686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.066672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:27.965625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.114271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.116956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.222466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:07.993971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.103808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:12.115893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.168042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.158005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.247708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.170900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:22.110787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.245060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.193715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.101217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.255944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.257881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.369534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:08.140235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.252402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:12.258657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.312395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.303987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.388075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.310750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:22.252442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.395730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.330220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.241767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.402149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.404329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.503477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:08.271830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.393712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:12.491169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.435904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.435143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.516873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.441709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:22.376488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.531684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.450571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.369559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.533787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.533168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.642160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:08.419586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.536358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:12.629117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.573918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.572347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.651845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.585754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:22.509601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.670410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.580399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.502652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.675047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.671263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.789351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:08.673263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.684438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:12.771719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.713673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.727355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.791843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.725331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:22.649798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.822870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.716143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.642719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.821878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.815588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:34.937765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:08.816411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:10.827443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:12.914714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:14.849402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:16.877734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:18.931358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:20.864922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:22.790933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:24.973828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:26.853283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:28.783243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:30.970593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-03-04T15:32:32.961568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-03-04T15:32:45.464016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-04T15:32:45.775267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-04T15:32:46.074555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-04T15:32:46.373453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-04T15:32:46.668786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-04T15:32:35.315535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-04T15:32:36.421288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-04T15:32:36.838603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-04T15:32:37.078713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

admission_idpatient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedreadmitted
0100706.012224646.0CaucasianMale[40-50)?2.01.013BCEndocrinologyFalseComplete64.0010.0000577250.024015A+14.2FalseNone>8NoYesNoYesFalse
199518.01020438.0African AmericanMale[60-70)?2.018.022??FalseComplete32.0115.04044285854969O-14.1FalseNoneNoneNoYesNoYesFalse
289397.0141934014.0AfricanAmericanFemale[60-70)?3.01.015BCFamily/GeneralPracticeFalseComplete57.0230.00005625965859A+13.4FalseNONENoneNoYesNoYesFalse
389653.0168821964.0CaucasianMale[50-60)?3.01.011MCRadiologistFalseComplete34.0624.00014144112959O-15.3FalseNoneNoneYesYesChYesFalse
483278.06485868.0HispanicMale[30-40)?1.03.0714??FalseComplete89.0648.00008088618659O+14.6TrueNoneNoneNoYesChYesFalse
583713.0123423750.0CaucasianMale[40-50)?1.06.077??FalseComplete3.0011.00014344254289A+15.4FalseNONENoneNoYesChYesFalse
6101072.0114696756.0CaucasianMale[60-70)?1.01.072MC?FalseComplete44.0017.0000852NaN4149A+14.8FalseNoneNoneNoYesChYesTrue
790645.011351394.0CaucasianFemale[60-70)?3.03.013?OrthopedicsFalseComplete26.0132.00017152504279A+14.2FalseNoneNoneNoYesNoYesFalse
897867.0141851502.0WHITEMale[80-90)?6.03.074?InternalMedicineFalseComplete41.008.0000823920E8886O+18.0FalseNormNoneNoYesNoYesFalse
989885.0140092992.0CaucasianMale[70-80)?1.01.073??FalseComplete44.0013.00004102504389O+15.6FalseNONE>7NoYesChYesFalse

Last rows

admission_idpatient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedreadmitted
968481825.0165320082.0WHITEFemale[70-80)?1.01.072MCInternalMedicineFalseComplete60.0011.0000250.84915999O+14.0FalseNoneNoneNoNoNoYesFalse
968596103.084284514.0CaucasianMale[70-80)?1.03.073MC?FalseComplete53.0018.0001435250.022539O+17.4FalseNone>8NoYesChYesFalse
968696970.087469974.0CaucasianFemale[60-70)?3.01.017MC?FalseComplete21.0411.00001534012504O+13.0FalseNoneNoneNoYesChYesFalse
968799920.082030878.0CaucasianMale[60-70)?1.01.072SP?FalseComplete37.018.0001531285417A-15.1FalseNoneNoneNoNoNoNoFalse
968888037.012464442.0?Male[70-80)?2.01.092MCInternalMedicineFalseComplete41.0015.00004282504015A+14.0FalseNONENoneNoYesChYesFalse
968991851.017594028.0CaucasianFemale[70-80)?1.018.073?Family/GeneralPracticeFalseIncomplete52.007.0000562428V459A+13.0FalseNoneNoneNoNoNoNoFalse
969084067.046722582.0CaucasianMale[80-90)?6.025.0177?Family/GeneralPracticeFalseComplete25.0015.02029974534969B+15.2FalseNormNoneNoNoNoYesFalse
969185961.083528442.0CaucasianMale[70-80)?NaN1.013MCFamily/GeneralPracticeFalseIncomplete45.007.00004284014275O-14.9FalseNoneNoneNoNoNoYesFalse
969289365.0119906946.0CaucasianMale[80-90)?1.01.071MC?FalseIncomplete3.00NaN0004354284019O+14.5FalseNoneNoneNoYesChYesFalse
969382858.0103427046.0LatinoFemale[60-70)?1.01.012?InternalMedicineFalseComplete44.008.0001438780V456B+13.5FalseNoneNormNoNoNoNoTrue